Search Results (5,127)

In the context of escalating implementation of electric vehicles (EVs), further research is required to investigate the impact of empirical driving conditions on energy demand and battery performance. In spite of the fact that microscopic traffic simulation and EV energy modelling are already used extensively, their use is still limited in studies focusing on batteries. Specifically, in most existing approaches, the effect of traffic-induced variability on battery stress is not explicitly accounted for. The study presented here examines a systematic framework that combines energy demand, traffic dynamics and battery behaviour. Using the SUMO simulator, vehicle trajectories are converted into electric vehicle (EV) energy profiles via a physics-based longitudinal model, thereby estimating battery power, energy consumption, regenerative effects and changes in state of charge (SOC). Next, a variety of indicators related to the battery management system (BMS) are introduced, including the Battery Stress Index (BSI), a traffic–energy severity (TES) indicator and event-based measures for transient conditions. The results show that traffic variability leads to significant fluctuations in battery load, which are not fully captured by conventional energy metrics. The proposed indicators provide additional information on cumulative and dynamic battery solicitation while remaining physically interpretable. Taken together, this framework links traffic conditions and battery solicitation in a coherent approach, thereby creating a scalable approach to traffic-aware energy analysis. Full article

This paper investigates vehicle-to-vehicle (V2V) integrated sensing and communication (ISAC) networks under ultra-reliable low-latency communication (URLLC) constraints in the presence of a vehicular eavesdropper (VE). To address the lack of instantaneous eavesdropper channel state information (CSI) in high-mobility scenarios, a vehicular jammer (VJ) is employed to perform radar-based sensing and extended Kalman filter (EKF)-based tracking of the VE’s kinematic state. The estimated state information and its posterior uncertainty are shared with the legitimate transmitter and are used to construct uncertainty-aware spatial covariance matrices for the VE-related channels. Based on these covariance matrices, the VJ transmits artificial noise (AN) in the null space of the legitimate receiver, thereby degrading the VE’s reception while avoiding interference to the intended link. In this context, a finite-blocklength (FBL) secrecy-rate framework is developed together with a two-time-scale optimization strategy, where the sensing resources are optimized at the slot level to enhance EKF tracking accuracy, while the transmit covariance and AN covariance matrices are optimized at the frame level to maximize the average FBL secrecy rate. The resulting non-convex problem is handled through semidefinite relaxation (SDR), alternating optimization (AO), and successive convex approximation (SCA). Simulation results show that the proposed framework improves secrecy robustness against mobility and sensing-induced spatial uncertainty. Full article

Mobile platforms such as high-speed trains and unmanned aerial vehicles (UAVs) experience quasi-periodic variation in link quality as they move through a cellular base-station lattice, so the service rate of their on-board uplink buffer is itself time-periodic. We model this buffer as a periodic $M/M\left(t\right)/1$ queue whose service rate follows from a signal-to-noise-ratio (SNR)-to-rate map and construct an edge-resident controller that exploits this periodic structure for real-time quality-of-service (QoS) control. From a harmonic-balance (Fourier–Galerkin) solution of the periodic regime, the controller derives backlog and tail-probability indicators and uses them to drive admission, redundancy and handover decisions on the device. The method rests on a stability criterion and a quantitative error bound for the spectral truncation, under stated regularity and stability conditions, and is validated against Monte Carlo simulation along a ∼650 km geo-anchored corridor: on the periodic backbone, the solver matches simulation to within about $1.6\%$, and a coefficient-driven admission rule lowers the 99th-percentile delay by about $28\%$ relative to a reactive baseline at high load. On the full map-derived profile with aperiodic coverage gaps, the proposed proactive controller—spectral backbone admission combined with a radio-map look-ahead—attains the lowest mean and tail delay, about $27\%$ and $21\%$ below the reactive baseline and $54\%$ and $42\%$ below uncontrolled DropTail, with buffer overflow cut from $2.2\%$ to $0.1\%$, at a deliberate admitted-load cost (goodput ≈0.84 vs. $0.94$). An operation-count analysis indicates compatibility with sub-$100$ms control deadlines on a Cortex-A55-class system-on-chip. The controller runs on the device itself, without cloud or GPU, and the architecture is realised in a granted patent; end-to-end hardware benchmarking and an extension to non-Poisson traffic are left for future work. Full article

Urban traffic congestion remains a major barrier to sustainable mobility, necessitating a transition from static signaling to Intelligent Traffic Light Systems (ITLS). This study presents a systematic review in computer vision, artificial intelligence, and adaptive control algorithms for urban intersection optimization. Following a methodological framework informed by PRISMA guidelines, the study examines state-of-the-art architectures, including deep learning-based perception models and reinforcement learning agents. The findings indicate that, although two-stage detectors provide benchmark accuracy for vehicle perception, single-stage models and Vision Transformers offer the high-speed processing required for real-time traffic management. In addition, deep reinforcement learning enables autonomous, lane-specific optimization that outperforms traditional actuated and fixed-time systems. The review also identifies a persistent research gap in the deployment of these computational frameworks in the resource-constrained and heterogeneous infrastructures of developing countries. For the urban context of Riobamba, Ecuador, a phased implementation strategy is proposed that balances computational demands with the city’s morphological and social characteristics. By bridging the gap between high-fidelity simulation and practical field deployment, this review provides a scalable framework for improving throughput, reducing emissions, and enhancing safety. Overall, these advances offer a promising pathway toward more resilient transportation systems in rapidly evolving Andean urban centers. Full article

Autonomous driving systems relieve drivers from continuous vehicle operation and constant monitoring, allowing them to engage in non-driving-related tasks (NDRTs). However, immersion in such tasks can impair drivers’ perception of both the takeover situation and the surrounding environment. To quantitatively assess drivers’ risk perception capability during takeover, a driving simulation platform was used to design autonomous takeover scenarios involving three types of NDRTs, three takeover request times (TOR), and two obstacle avoidance conditions. A total of forty participants were recruited to complete the driving experiment. Drivers’ eye movement data were collected, and visual metrics—including fixation, saccade, and pupil diameter—were extracted by defining areas of interest (AOIs). A subjective risk perception scale was developed and administered to measure drivers’ subjective evaluations. Together with takeover reaction time, the K-means clustering method was applied to classify drivers’ risk perception levels into three categories: high, medium, and low. The LightGBM algorithm was selected to construct a baseline classification model for assessing drivers’ risk perception levels. Subsequently, the Whale Optimization Algorithm (WOA) was employed to optimize the hyperparameters of LightGBM, resulting in the WOA-LightGBM model. This optimized model demonstrated improved recall, accuracy, precision, and F1-score, reaching 0.9210, 0.9253, 0.9261, and 0.9201, respectively. Furthermore, SHapley Additive exPlanations (SHAP) analysis was conducted to quantify the contribution of eye movement indicators to risk perception assessment. The results revealed that saccade duration in the NDRT areas significantly reduced drivers’ risk perception levels (SHAP value = −0.71), whereas increased saccade duration in the forward road area effectively restored drivers’ risk perception capability (SHAP value = 0.71). In addition, higher risk perception levels were found to enhance drivers’ takeover performance in terms of vehicle control. These findings provide valuable insights for the management of NDRTs and the optimization of autonomous vehicle takeover systems. Full article

Ports are critical nodes in global supply chains and increasingly depend on intelligent, energy-efficient, and autonomous technologies to improve operational performance and sustainability. Within this context, unmanned aerial vehicles (UAVs) are evolving from isolated aerial platforms into network-enabled systems capable of supporting sensing, communication, computation, and decision-support functions. This study examines the evolution of energy-efficient UAV-enabled systems for sustainable port logistics, with particular emphasis on autonomous system architectures, mobile edge computing, artificial intelligence, optimisation, and Internet of Things integration. Following PRISMA 2020 reporting guidelines, a bibliometric and systematic review was conducted based on a curated dataset of 49 peer-reviewed publications indexed in Scopus between 2001 and 2026. The analysis combines performance indicators with science mapping techniques, including bibliographic coupling and keyword co-occurrence, to identify research trends, influential contributions, and thematic structures. The results reveal a transition from fragmented exploratory studies to a rapidly expanding research field shaped by UAV-assisted edge computing, resource allocation, intelligent optimisation, and energy-aware system design. Four main thematic clusters are identified: UAV-assisted mobile edge computing and network optimisation; advanced optimisation and intelligent UAV systems; energy efficiency and resource allocation; and application-oriented developments and hardware innovations. The findings indicate that energy efficiency is the central design principle connecting UAV autonomy, system integration, and sustainability-oriented port logistics. Full article

Smart and sustainable mobility increasingly relies on distributed sensing, low-power communication technologies, and cloud-based ICT platforms. This article presents a comprehensive scientific analysis of the technological foundations of sensitized mobility, reviewing the state of the art in embedded sensing, distributed systems, and communication paradigms for future mobility challenges. The research project “MOST” and in particular its subgroup “Spoke 5” falls within this framework of sustainable and sensorized mobility, with numerous activities in data collection, analysis, and field experimentation. In order to allow data collection, retention and analysis, one of the challenges that we must address is the definition of an adequate ICT architecture. The core contribution of this work is the presentation of the MOST ICT architecture, designed as a containerized, scalable, and resilient infrastructure capable of integrating heterogeneous data coming from field-deployed systems. In addition, it discusses the primary research challenges encountered in the definition and development of the presented architecture by examining two representative case studies within the MOST-Spoke 5 research project: renewable energy charging stations for light electric vehicles and cyclists monitoring systems. Full article

Satellite–terrestrial integrated networks (STINs) can extend remote sensor telemetry, remote Internet of Things (IoT), and emergency communication services beyond terrestrial coverage, but their evaluation is complicated by heterogeneous mobility, channel, resource, and control-plane dynamics. This study presents a software-based modular testbed and performance-emulation framework for STINs. The framework integrates scenario generation, model-driven data processing, replaceable algorithm engines, scheduler-based execution control, and a Kafka-style message interface. It models terrestrial, unmanned aerial vehicle, and low-Earth-orbit satellite entities and provides link-budget abstraction, access control, mobility-aware handover, traffic generation, scheduling, load balancing, adaptive routing, and multi-mode transmission for mixed sensing and communication traffic. The representative strategies are evaluated using a lightweight emulation model parameterized by standards-informed NTN and link-budget assumptions. Representative results reveal tradeoffs between access, handover, routing, and scheduling strategies, together with sensitivity to workload, mobility, outage, demand, and selected model parameters. The proposed framework therefore supports traceable STIN strategy evaluation for remote sensor networks, sensing-data backhaul, and remote-IoT service scenarios under explicit emulation assumptions. Full article

Autonomous mobile robots depend on sensing and state estimation to provide feedback, while their controllers must remain effective under process disturbances and model mismatch. Reinforcement learning-based model predictive control (RL-MPC) learns the terminal cost online, while the deterministic RLMPC baseline uses a nominal stage cost. This study considers the control layer and assumes that the robot state is available; measurement noise and state-estimation errors are outside the modeled disturbance channel. Isotropic state regularization is introduced into the RL-MPC stage cost, yielding value-regularized RLMPC (VR-RLMPC). The same $\mathit{\beta }$ term changes both the physical state penalty and the N-step terminal-value learning target without adding online optimization variables, constraints, or sampling operations. Under explicit value-function, feasibility, domain-containment, and bounded-disturbance assumptions, a Lyapunov-drift analysis yields a conditional one-step expected-drift bound outside an explicit radius. Simulations on linear and nonlinear nonholonomic vehicle systems show empirical VFA weight-update settling-step indices (CR) that are approximately 25% lower than those of RLMPC across the nominal and five model-mismatch comparisons. Across the tested mismatch range, VR-RLMPC has a worst-case performance degradation rate of 2.8%, compared with 25–49% for conventional MPC baselines; its cost improvement also increases with task difficulty. In the tested nominal setting, VR-RLMPC also outperforms an explicit stochastic temporal-difference baseline without adding online optimization variables or sampling operations. Full article

Vehicle-to-Everything (V2X) communication is an essential building block of intelligent transportation systems, supporting high-data-rate vehicle-to-infrastructure (V2I) services, and ultra-reliable low-latency vehicle-to-vehicle (V2V) communication. However, in dense urban environments, V2X services can be significantly degraded by the presence of severe blockage, fast channel variations, and high levels of interference. Intelligent Reflecting Surfaces (IRSs) can be employed to reconfigure wireless propagation environments to improve V2X communication. However, the joint optimization of transmit power, spectrum reuse, and IRS reflection coefficients is a mixed-integer non-linear problem, which is further complicated by the fast vehicular mobility and time-varying interference in V2X networks. To tackle this challenging problem, this work proposes a scalable and deployable decentralized hierarchical multi-agent deep reinforcement learning (DH-MDRL) framework. The key design principle is the separation of control timescales, whereby each V2V link functions as an autonomous agent that responds to local observations at a fast timescale and determines its transmit power and spectrum reuse decisions, while the IRS controller at the base station (BS), using global network observations, updates the IRS reflection coefficients at a slower timescale. This hierarchical architecture reduces coordination signaling associated with centralized resource allocation while enabling distributed resource allocation. The IRS-assisted V2X network is modeled as a Markov decision process, where the reward design is tailored to optimize the V2I sum data rate while guaranteeing the latency and reliability constraints associated with safety-critical V2V communication. Simulation results show that the proposed DH-MDRL framework outperforms conventional schemes without IRSs and achieves an excellent trade-off between V2V link constraints’ satisfaction probability and V2I link sum data rates compared to centralized resource allocation approaches. Full article

Autonomous vehicles (AVs) are moving from pilots to regular urban service, yet the speed of large-scale implementation remains uncertain. While prior research emphasizes technological feasibility and adoption, less attention has been paid to the socio-technical dynamics that constrain deployment. This study examines how unintended consequences shape the pace of AV implementation in cities. Drawing on a mixed-methods design combining a structured scoping review with 18 expert interviews, interrelated dynamics are identified across institutional, behavioral, economic-platform, spatial, and normative-societal domains. The findings indicate that implementation speed is not determined by technology alone but emerges from reinforcing feedback loops that generate systemic frictions, including governance lag, demand rebound, spatial bottlenecks, and legitimacy challenges. The study advances a systems-oriented framework that conceptualizes implementation speed as an emergent property of socio-technical dynamics, highlighting the importance of adaptive and anticipatory governance for sustainable urban mobility transitions. Full article

Electric vehicles (EVs) are increasingly regarded as a key component of low-carbon mobility and the sustainable energy transition. However, their large-scale deployment raises challenges that extend beyond vehicle technologies and require a system-level understanding of interactions with power networks, energy resources and users. This paper presents a critical review of the literature published since 2012, examining EV development from an integrated energy perspective that includes vehicle technologies, charging infrastructure, power electronics, grid integration, renewable energy coupling and environmental implications. A structured methodology is used to identify and analyze peer-reviewed studies, with particular emphasis on high-impact review articles that consolidate knowledge across disciplines. The analysis shows that, despite significant technological progress, large-scale EV deployment remains constrained by infrastructure limitations, distribution grid readiness, charging coordination strategies, material availability and socio-technical factors. Simulation-based studies play a central role in anticipating these impacts and informing deployment strategies before real-world implementation. Rather than addressing individual components in isolation, this review highlights interdependencies between technologies, control approaches and energy systems. Based on this synthesis, key research priorities and high-level challenges are identified, providing guidance for future research and policy aimed at enabling EVs to effectively support sustainable ambient energy and mobility systems worldwide deployment. Full article

Achieving the European Union’s (EU-27) 2050 climate neutrality goal requires a drastic reduction in transport emissions. This study utilizes the pymedeas2 integrated assessment model to evaluate trade-offs between technology-led and structure-led transitions under both continuous growth and steady-state economic paradigms. Our results reveal that relying primarily on private vehicle electrification falls short of emission targets. The baseline REF-G scenario—following current institutional roadmaps centred on rapid technological substitution and sustained economic growth—maintains a high final energy demand and requires a cumulative extraction of 2.35 Mt of lithium by 2050, claiming nearly 6.4% of current global proven reserves solely for European mobility. Conversely, combining a modal shift toward electrified rail with macroeconomic stabilization (RAIL-SSE) reduces transport final energy demand by 68% relative to the projected 2024 peak and decreases lithium requirements by 57%. This sufficiency-driven pathway achieves the deepest absolute climate mitigation, dropping residual transport emissions to approximately 90 MtCO₂/year. Furthermore, despite the front-loaded costs of rail expansion, RAIL-SSE emerges as the least capital-intensive pathway, requiring a total investment of USD 19.83 trillion—a systemic saving of USD 7.27 trillion relative to the REF-G baseline. We conclude that reaching absolute sustainability in the EU transport sector necessitates a policy shift away from resource-intensive green growth strategies toward demand sufficiency and durable public infrastructure. Full article

Vehicle Ad Hoc Networks (VANETs) face critical challenges in trust management, privacy preservation, and scalability, particularly with the integration of 5G networks in Intelligent Transportation Systems (ITS). Traditional centralized trust models present single points of failure and privacy concerns that compromise network security and user anonymity. This paper presents a novel decentralized trust model leveraging blockchain technology, Interplanetary File System (IPFS) integration, and post-quantum cryptographic algorithms to address these limitations. Our proposed TrustChain-VANET framework implements advanced privacy-preserving encryption techniques including threshold and homomorphic encryption, geographical sharding for scalability, and edge-assisted consensus mechanisms. Performance evaluation demonstrates significant improvements: 40% reduction in authentication latency (90–120 ms vs. 150–300 ms), 90% malicious node detection rate (+15% improvement), 300% increase in transaction throughput (2000–2150 TPS), and 100% scalability enhancement supporting up to 5000 nodes. The system integrates seamlessly with 5G network slicing (URLLC, eMBB, mMTC) while maintaining quantum resistance through CRYSTALS-Dilithium, KYBER, and FALCON algorithms. Real-world deployment considerations including OBU computational constraints, standardization gaps, and energy efficiency are comprehensively analyzed. Results indicate that the proposed decentralized approach provides robust security, enhanced privacy, and improved scalability for next-generation vehicular networks, making it suitable for large-scale ITS deployment. The main contribution of this work is the development of a unified TrustChain-VA 48NET framework. The proposed framework integrates blockchain-based trust management, IPFS-assisted storage, 5G network slicing, Mobile Edge Computing (MEC), geographical sharding, and post-quantum cryptographic mechanisms within a single architecture for next-generation VANET environments. While these technologies have been investigated separately in previous studies, this work presents a consolidated framework that analyzes their interoperability, identifies integration challenges, and evaluates their combined impact on trust management, scalability, privacy preservation, and deployment feasibility in Intelligent Transportation Systems. Full article

In dynamic Vehicular Edge Computing (VEC) environments, rapidly changing vehicle mobility and traffic lead to fluctuating edge resource demands, challenging task offloading and scheduling. Accurate prediction of future traffic flow and traffic-state-derived workload representations is thus crucial for proactive resource management. To address the limitations of existing methods in short-term dynamic characterization, complex spatial interaction modeling, and heterogeneous target prediction, this paper proposes a Spatio-Temporal Graph Attention Network (TA-STGAT). The proposed model constructs multi-dimensional RSU-level state sequences from simulated trajectories generated on a real-world road network and separately forecasts vehicle flow within RSU coverage areas and the associated traffic-state-derived workload representation under a unified spatio-temporal modeling framework. By integrating gated dilated temporal convolutions with a topology-constrained multi-head graph attention mechanism, the model captures multi-scale temporal dependencies and nonlinear spatial correlations. Experimental results show that, compared with the best-performing baseline in terms of RMSE for each forecasting task, TA-STGAT reduces RMSE by 10.89% and 11.29% in workload-representation prediction and traffic flow prediction, respectively, demonstrating its effectiveness for short-term edge-state forecasting. Full article